HR: 17:00h
AN: B24B-05    [Abstracts]
TI: Global sensitivity analysis of Leaf-Canopy radiative transfer Model for analysis and quantification of uncertainties in remote sensed data product generation
AU: * Furfaro, R
EM: robertof@email.arizona.edu
AF: Aerospace and Mechanical Engineering Department, Univerisity of Arizona, 1130 N. mountain, tucson, AZ 85721, United States
AU: Morris, R D
EM: robin.morris@gmail.com
AF: USRA-RIACS, 444 Castro St, Suite 320, Mountain View, CA 94041, United States
AU: Kottas, A
EM: thanos@soe,ucsc.edu
AF: Department of Applied Mathematics and Statistics, University of California, 1156 High Street, Santa Cruz, CA 95064, United States
AU: Taddy, M
EM: taddy@soe.ecsc.edu
AF: Department of Applied Mathematics and Statistics, University of California, 1156 High Street, Santa Cruz, CA 95064, United States
AU: Ganapol, B D
EM: ganapol@cowboy.ame.arizona.edu
AF: Aerospace and Mechanical Engineering Department, Univerisity of Arizona, 1130 N. mountain, tucson, AZ 85721, United States
AB: Analyzing, quantifying and reporting the uncertainty in remote sensed data products is critical for our understanding of Earth's coupled system. It is the only way in which the uncertainty of further analyses using these data products as inputs can be quantified. Analyzing the source of the data product uncertainties can identify where the models must be improved, or where better input information must be obtained. Here we focus on developing a probabilistic framework for analysis of uncertainties occurring when satellite data (e.g., MODIS) are employed to retrieve biophysical properties of vegetation. Indeed, the process of remotely estimating vegetation properties involves inverting a Radiative Transfer Model (RTM), as in the case of the MOD15 algorithm where seven atmospherically corrected reflectance factors are ingested and compared to a set of computed, RTM-based, reflectances (look-up table) to infer the Leaf Area Index (LAI). Since inversion is generally ill-conditioned, and since a-priori information is important in constraining the inverse model, sensitivity analysis plays a key role in defining which parameters have the greatest impact to the computed observation. We develop a framework to perform global sensitivity analysis, i.e., to determine how the output changes as all inputs vary continuously. We used a coupled Leaf-Canopy radiative transfer Model (LCM) to approximate the functional relationship between the observed reflectance and vegetation biophysical parameters. LCM was designed to study the feasibility of detecting leaf/canopy biochemistry using remote sensed observations and has the unique capability to include leaf biochemistry (e.g., chlorophyll, water, lignin, protein) as input parameters. The influence of LCM input parameters (including canopy morphological and biochemical parameters) on the hemispherical reflectance is captured by computing the "main effects", which give information about the influence of each input, and the "sensitivity indices", i.e., the expected amount by which the uncertainty in the output is reduced if the true value of a specific input was known. Since RTMs are generally computationally expensive, we develop a Gaussian Process (GP) statistical model to approximate the LCM output surface. Once the GP parameters are estimated (using simulated data from the LCM model), the computation of main effects and sensitivity indices is straightforward. Using this approach, we were able to quantify the importance of LCM input parameters for each of the eight wavelengths centered on the MODIS bands commonly used to observe vegetation (visible and Near-Infrared or NIR). We found that chlorophyll dominates the visible region while LAI has the largest effect on the NIR. Surprisingly, we also found that lignin is sensitive in short-wave infrared (1640nm and 2310nm) where it is the main contributor to the overall reflectance. These results indicate the feasibility and utility of probabilistic uncertainty and sensitivity analysis for RTMs. The developed methodology can be used both to improve RTMs for better measurement predictions, and to guide the data collection or land cover to reduce the level of uncertainty.
DE: 0430 Computational methods and data processing
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting